HED augmentations for digital pathology image

[Geeks-Sid]

Added H&E based augmentations for Histopathology images (can be used for other RGB images)
Solves #510

Proposed Changes

• Added a hed transform that will allow to make precise changes to digital pathology images, particularly H&E images.

Checklist

• I have read the CONTRIBUTING guide.
• My PR is based from the current GaNDLF master .
• Non-breaking change (does not break existing functionality): provide as many details as possible for any breaking change.
• Function/class source code documentation added/updated.
• Code has been blacked for style consistency.
• If applicable, version information has been updated in GANDLF/version.py.
• If adding a git submodule, add to list of exceptions for black styling in pyproject.toml file.
• Usage documentation has been updated, if appropriate.
• History has been updated, if appropriate.
• Tests added or modified to cover the changes; if coverage is reduced, please give explanation.
• If customized dependency installation is required (i.e., a separate pip install step is needed for PR to be functional), please ensure it is reflected in all the files that control the CI, namely: python-test.yml, and all docker files [1,2,3].

[github-actions]

MLCommons CLA bot All contributors have signed the MLCommons CLA ✍️ ✅

[codecov]

Codecov Report

Merging #649 (7fe43db) into master (bca8bbd) will increase coverage by 0.04%.
The diff coverage is 97.05%.

❗ Current head 7fe43db differs from pull request most recent head 1cc449a. Consider uploading reports for the commit 1cc449a to get more accurate results

@@            Coverage Diff             @@
##           master     #649      +/-   ##
==========================================
+ Coverage   94.62%   94.67%   +0.04%     
==========================================
  Files         116      117       +1     
  Lines        8062     8162     +100     
==========================================
+ Hits         7629     7727      +98     
- Misses        433      435       +2     

Flag	Coverage Δ
unittests	94.67% <97.05%> (+0.04%)	⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
GANDLF/data/augmentation/rgb_augs.py	100.00% <ø> (ø)
GANDLF/data/augmentation/hed_augs.py	96.15% <96.15%> (ø)
GANDLF/data/augmentation/__init__.py	100.00% <100.00%> (ø)
GANDLF/parseConfig.py	84.53% <100.00%> (+0.75%)	⬆️
testing/test_full.py	99.16% <100.00%> (+<0.01%)	⬆️

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[Geeks-Sid]

@sarthakpati I am not able to hit the transform function, can you assist me with the code coverage.

[sarthakpati]

LGTM pending tests